Intelligent Video Surveillance System Using Faster Regional Convolutional Neural Networks

Abstract

Insecurity remains a major challenge in our society. Government, private organizations, and individuals strive to ensure their possessions are kept safe from intruders. Automated surveillance system plays a key role to ensure that the environment is safe with little human intervention. Therefore, object detection, classification, and tracking are vital in building a robust and remote intelligent video surveillance system to aid security in physical environments. Previous studies used enhanced background subtraction techniques for object detection which recorded notable achievements but performance issues in distinguishing humans, pets and vehicles. For insecurity to be solved more intelligently, deep neural network techniques are employed. In this paper, an intelligent video surveillance system that detects only human intrusion and sends an SMS notification to the user with the registered mobile number was developed. The results of the system performance evaluation recorded an accuracy of 96%, a precision of 94%, and a recall of 98%. The experimental results showed that the intelligent system was suitable for detecting human intrusion, thereby contributing to the safety of physical environments.